Cloud Resource Operations for Databases in Multiple Distributed Cloud Deployment Zones

1. A system for cloud resource operations for databases in multiple cloud deployment zones, the system comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to: obtain first data via a copy of an application executing in a first cloud deployment zone of the multiple cloud deployment zones, wherein the application has copies executing in respective cloud deployment zones of the multiple cloud deployment zones; write, to a first instance of a database executing in the first cloud deployment zone, the first data with a deployment zone identifier that indicates the first cloud deployment zone, wherein the multiple cloud deployment zones include respective instances of multiple instances of the database including the first instance, and wherein the multiple instances are configured to perform data replication across the multiple instances; generate a first event based on writing the first data to the first instance; and perform, via a cloud resource in the first cloud deployment zone, a learning operation using the first data for a machine learning model based on the first event and based on the first data including the deployment zone identifier that indicates the first cloud deployment zone.

2. The system of claim 1, wherein the one or more processors are further configured to: obtain an indication of a second event associated with second data that includes a deployment zone identifier that indicates a second cloud deployment zone; and refrain from performing, via the cloud resource, the learning operation using the second data based on the second data including the deployment zone identifier that indicates the second cloud deployment zone and based on the cloud resource being in the first cloud deployment zone.

3. The system of claim 1, wherein the one or more processors, to perform the learning operation, are configured to: provide, via the cloud resource, the first data to the machine learning model to cause the learning operation to be performed.

4. The system of claim 1, wherein the one or more processors are further configured to: filter, via the cloud resource, data obtained via updates to the first instance using the deployment zone identifier that indicates the first cloud deployment zone to filter out any data not originating from the first cloud deployment zone.

5. The system of claim 1, wherein the first event is associated with a set of data including the first data, and wherein the one or more processors are further configured to: trigger, based on generating the first event, the cloud resource to perform an operation; and remove, via the cloud resource, second data from the set of data that includes a deployment zone identifier that indicates a second cloud deployment zone based on the cloud resource being included in the first cloud deployment zone.

6. The system of claim 1, wherein the deployment zone identifier identifies a cloud deployment zone, of the multiple cloud deployment zones, in which data is originated.

7. The system of claim 1, wherein the learning operation includes a stochastic gradient descent operation that uses the data to train the machine learning model.

8. The system of claim 1, wherein the cloud resource includes a serverless function that is triggered by the first event.

9. A method of cloud resource operations for databases in multiple cloud deployment zones, comprising: obtaining, by a cloud system, data via a copy of an application executing in a first cloud deployment zone of the multiple cloud deployment zones, wherein the application has copies executing in respective cloud deployment zones of the multiple cloud deployment zones; storing, by the cloud system and in a database executing in the first cloud deployment zone, the data with a deployment zone identifier that indicates the first cloud deployment zone, wherein the multiple cloud deployment zones include respective databases of multiple databases including the database, and wherein the multiple databases are configured to perform multi-deployment zone replication; and performing, by the cloud system and via a cloud resource in the first cloud deployment zone, an operation using the data based on the data including the deployment zone identifier that indicates the first cloud deployment zone and based on the cloud resource being in the first cloud deployment zone.

10. The method of claim 9, wherein performing the operation comprises: transmitting, via the cloud resource, the data to a device.

11. The method of claim 9, wherein performing the operation comprises: providing, via the cloud resource, the data to a machine learning model to cause the machine learning model to be trained using the data.

12. The method of claim 9, wherein the multi-deployment zone replication includes storing replicated data in the database that includes a deployment zone identifier that indicates a second cloud deployment zone, and wherein performing the operation comprises: refraining from performing the operation using the replicated data based on the replicated data including the deployment zone identifier that indicates the second cloud deployment zone.

13. The method of claim 9, further comprising: removing, via the cloud resource, replicated data from the data used for the operation, wherein the replicated data is associated with the multi-deployment zone replication, and wherein the replicated data originates from a second cloud deployment zone.

14. The method of claim 9, wherein storing the data in the database causes an event to be generated, the method further comprising: triggering, based on the event, the cloud resource to perform the operation; and removing replicated data from the of data that includes a deployment zone identifier that indicates a second cloud deployment zone based on the cloud resource being included in the first cloud deployment zone.

15. The method of claim 9, wherein the deployment zone identifier identifies a cloud deployment zone, of the multiple cloud deployment zones, in which data is originated.

16. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a system, cause the system to: obtain first data via a copy of an application executing in a first cloud deployment zone of multiple cloud deployment zones, wherein the application has copies executing in respective cloud deployment zones of the multiple cloud deployment zones; write, to a database executing in the first cloud deployment zone, the first data with a deployment zone identifier that indicates the first cloud deployment zone, wherein the multiple cloud deployment zones include respective databases of multiple databases including the database, and wherein the multiple databases are configured to perform data replication across the multiple databases; generate a first event based on writing the first data to the database; and perform, via a cloud resource in the first cloud deployment zone, an operation using the first data for a machine learning model based on the first event and based on the first data including the deployment zone identifier that indicates the first cloud deployment zone.

17. The non-transitory computer-readable medium of claim 16, wherein the one or more instructions further cause the system to: obtain an indication of a second event associated with second data that includes a deployment zone identifier that indicates a second cloud deployment zone; and refrain from performing, via the cloud resource, the operation using the second data based on the second data including the deployment zone identifier that indicates the second cloud deployment zone and based on the cloud resource being in the first cloud deployment zone.

18. The non-transitory computer-readable medium of claim 16, wherein the one or more instructions, that cause the system to perform the operation, cause the system to: provide, via the cloud resource, the first data to the machine learning model to cause a learning operation to be performed.

19. The non-transitory computer-readable medium of claim 16, wherein the one or more instructions further cause the system to: filter, via the cloud resource, replicated data from the data using the deployment zone identifier that indicates the first cloud deployment zone to filter out any data not originating from the first cloud deployment zone.

20. The non-transitory computer-readable medium of claim 16, wherein the first event is associated with a set of data including the first data, and wherein the one or more processors are further configured to: trigger, based on generating the first event, the cloud resource to be activated; and remove, via the cloud resource, second data from the set of data that includes a deployment zone identifier that indicates a second cloud deployment zone based on the cloud resource being included in the first cloud deployment zone.